Links table
Abstract and 1 introduction
1.1. Spatial DVD (SDTS)
1.2. Applications
1.3. Various components of SDTS
1.4. The scope of this work and contributions
2. Related work and 2.1. Digital twins and variables
2.2. Spatial Digital Twin Studies
3. Building blocks of spatial digital twins and 3.1. Get and process data
3.2. Data modeling, storage and management
3.3. Huge data analysis system
3.4. Maps and intermediate programs based on geographic information systems
3.5. Main functional ingredients
4. Other related modern technologies and 4.1. Amnesty International and ML
4.2. Blockchain
4.3. Cloud computing
5. Challenges and future work, and 5.1. Acquire multi -defined and accurate data
5.2. NLP Spatial Information and 5.3. Measurement with databases and huge data platforms for SDT
5.4. Spatial visions and 5.5. Multi -media
5.6. Build an simulator environment
5.7. Imagine complex and varied interactions
5.8. Reducing security and privacy concerns
6. Conclusion and references
4. Other related modern technologies
The recent developments in AI and ML technology, along with Blockchain and cloud computing, have greatly enabled our ability to solve a wide range of problems across various fields. Next, we discuss the details of how these technologies affect the development and operation of the SDTS.
4.1. Amnesty International and ML
Artificial Intelligence Technologies/ML has an important role that it plays in developing and operating DTS and SDTS [93]. SDTS is complex systems that require advanced techniques to improve their performance and efficiency. AI/ML technologies are essential ingredients in this regard because they enable digital twins to learn from data, predict future results and provide recommendations for improvement [86, 92]. There are three main aspects of this contribution.
• Artificial intelligence technologies /mm can improve the various processes of spatial digital twins [87]. For example, in smart buildings, AI/ML can predict energy consumption and improve energy use to reduce costs and emissions. In smart cities, AI/ML can improve transportation, reduce congestion, and enhance road safety [94]. In manufacturing, AI/ML can improve production processes, reduce waste, and increase efficiency.
• AI/ML technologies can also be used for predictive maintenance in spatial twins [95]. By analyzing data in the actual time of sensors and other devices, artificial intelligence/mm can predict when maintenance is required for equipment, thus avoiding costly stopping and improving the length of assets.
• In addition to improving operations and maintenance, AI/ML can also enhance the user experience of spatial digital twin through improved recommendations [96]. Amnesty International /m can customize user experiences, predict user preferences and improve user satisfaction in general.
4.2. Blockchain
SDTS, which depends on data -based software applications, is expected to see a growing use of the Distributed Professor Book Technologies (DLTS), which is usually referred to as Blockchain, to enhance the merit of data within it. Blockchain is a distributive system [97]. DLTS provides basic capabilities that can benefit greatly from SDTS, such as obtaining trust, processing and storage data. Blockchain supports a network of computers to store and update transactions and update (i.e. Professor’s book). ETHEREUM, Hyper Fabric and Bitcoins are some DLTS applications that can be considered for their suitability to provide SDTS confidence. However, there are several other points of concerns that must be evaluated before combining DLTS into SDTS.
Although Blockchains functions and characteristics can provide a reliable support system to ensure confidence, it is important to evaluate whether or not the appropriate conditions for SDTS. Some areas of exploration can be the suitability of Blockchain types, for example, in general/general, and the nature of consensus protocols, for example, proof of work (POW) or the proof of qualification (POA), and understanding the properties of Blockchain networks that store and operate Blockchains. For example, the performance, resource consumption and availability are the main features that must be studied because these are not fixed features of Blockchains but can vary based on how to design Blockchain network that becomes very vital. There is an increasing amount of scientific and commercial literature and the available products that can help in any type of feasibility and/or experiment to assess possible mechanisms and benefits to use DLTS for SDTS.
4.3. Cloud computing
SDTS is expected to be supported by technological infrastructure widely for computing, storage and networks. The support infrastructure is also expected to meet the requirements of quality features such as expansion, cumin, security and availability. With the entitlement and adoption of virtual infrastructure techniques such as cloud/fog, containers, and the virtual network simulation (NFV) [98]SDTS can effectively harness the capabilities of cloud computing technology. Looking at a large number of cloud services provided by public cloud services providers such as Amazon Web Services and MS Azure, in addition to the special cloud infrastructure, it becomes important to understand technological models and work available comprehensively to gain and use virtual infrastructure services to support SDTS. For example, infrastructure technology models such as IAAS, the platform such as PAAS and software as a (saas) service. Cloud Technologies primarily offers three main publishing models for storing and processing data and networks: private, general, hybrid. All technological and commercial models have their own positives and negatives. For example, the public cloud can be expensive compared to the private cloud, which is also relatively safer. However, it is expected that a special cloud organization will have a variety of knowledge and experience to design, implement and operate the special cloud that meets job and non -functional requirements for SDTS; The use of commercial cloud infrastructure does not require knowledge and experience requirements.
Authors:
(1) Muhammad Yunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh;
(2) Mohamed Amer Cheima, College of Information Technology, Monash University, 20 Walk Exhibitions, Clayton, 3164, VIC, Australia;
(3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Ece Building, Dhaka, 1000, Bangladesh;
(4) Anwar Olag, College of Computing, Charles Stort University, Port Makari, 2444, New South Wales, Australia;
(5) Muhammad Ali Babar, College of Computer and Sports Science, Adelaide University, Adelaide, 5005, S, Australia.
